Quantifying tissue optical properties of human heads in vivo using continuous-wave near-infrared spectroscopy and subject-specific three-dimensional Monte Carlo models

Significance: Quantifying subject-specific optical properties (OPs) including absorption and transport scattering coefficients of tissues in the human head could improve the modeling of photon propagation for the analysis of functional near-infrared spectroscopy (fNIRS) data and dosage quantificatio...

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Bibliographic Details
Published in:Journal of biomedical optics Vol. 27; no. 8; p. 083021
Main Authors: Kao, Tzu-Chia, Sung, Kung-Bin
Format: Journal Article
Language:English
Published: Bellingham Society of Photo-Optical Instrumentation Engineers 01.08.2022
SPIE
S P I E - International Society for
Subjects:
Alzheimer's disease
Approximation
Artificial neural networks
Brain
Cerebrospinal fluid
Computer simulation
Continuous radiation
Curve fitting
Forehead
Head
Infrared analysis
Infrared spectra
Infrared spectroscopy
Interpolation
Iterative methods
Light
Lookup tables
Magnetic resonance imaging
Mathematical models
Medical imaging
Modelling
Monte Carlo method
Near infrared radiation
Neural networks
Optical properties
Photons
Propagation
Reflectance
Scattering coefficient
Sensors
Simulation
Special Section Celebrating 30 Years of Open Source Monte Carlo Codes in Biomedical Optics
Spectrum analysis
Substantia grisea
Technology application
Therapeutic applications
Three dimensional models
Tissues
Monte Carlo method
optical properties
near-infrared spectroscopy
tissues
ISSN:1083-3668, 1560-2281, 1560-2281
Online Access:Get full text
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Summary:Significance: Quantifying subject-specific optical properties (OPs) including absorption and transport scattering coefficients of tissues in the human head could improve the modeling of photon propagation for the analysis of functional near-infrared spectroscopy (fNIRS) data and dosage quantification in therapeutic applications. Current methods employ diffuse approximation, which excludes a low-scattering cerebrospinal fluid compartment and causes errors. Aim: This work aims to quantify OPs of the scalp, skull, and gray matter in vivo based on accurate Monte Carlo (MC) modeling. Approach: Iterative curve fitting was applied to quantify tissue OPs from multidistance continuous-wave NIR reflectance spectra. An artificial neural network (ANN) was trained using MC-simulated reflectance values based on subject-specific voxel-based tissue models to replace MC simulations as the forward model in curve fitting. To efficiently generate sufficient data for training the ANN, the efficiency of MC simulations was greatly improved by white MC simulations, increasing the detectors’ acceptance angle, and building a lookup table for interpolation. Results: The trained ANN was six orders of magnitude faster than the original MC simulations. OPs of the three tissue compartments were quantified from NIR reflectance spectra measured at the forehead of five healthy subjects and their uncertainties were estimated. Conclusions: This work demonstrated an MC-based iterative curve fitting method to quantify subject-specific tissue OPs in-vivo, with all OPs except for scattering coefficients of scalp within the ranges reported in the literature, which could aid the modeling of photon propagation in human heads.
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ISSN:1083-3668
1560-2281
1560-2281
DOI:10.1117/1.JBO.27.8.083021